system

Abstract

The system according to this embodiment aims to efficiently collect and analyze employee opinions and propose appropriate improvement measures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects employee opinions. The analysis unit analyzes the opinions collected by the collection unit, compiles the results, and creates a report for reporting. The proposal unit proposes improvement measures based on the results obtained by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently collect and analyze the opinions of employees and propose appropriate improvement measures.

[0005] The system according to the embodiment aims to efficiently collect and analyze the opinions of employees and propose appropriate improvement measures.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the opinions of employees. The analysis unit analyzes the opinions collected by the collection unit, aggregates the results, and creates a report for reporting. The proposal unit proposes improvement measures based on the results obtained by the analysis unit.

Advantages of the Invention

[0007] The system according to this embodiment can efficiently collect and analyze employee opinions and propose appropriate improvement measures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The employee satisfaction survey system according to an embodiment of the present invention is a system that grasps employee opinions in real time and proposes effective improvement measures. This system collects employee opinions, an AI analyzes and aggregates the results, and creates a report for reporting. Furthermore, the AI ​​proposes specific improvement measures by referring to successful cases in other organizations. For example, open-ended questions and an anonymous suggestion box are added to collect employee opinions. Employees can freely write their opinions through open-ended questions in the monthly survey. In addition, an anonymous suggestion box is set up where employees can always submit their opinions, allowing them to submit complaints and requests anonymously. This makes it possible to collect detailed opinions from employees. Next, the AI ​​analyzes the collected opinions, aggregates the results, and creates a report for reporting. The AI ​​analyzes the collected opinions and generates data to understand the state of the organization. For example, it classifies employee opinions and identifies common problems and areas for improvement. This allows organizational managers and HR personnel to understand the state of the organization in real time. Furthermore, the AI ​​proposes specific improvement measures by referring to successful cases in other organizations. The AI ​​learns from successful cases in other organizations and proposes optimal improvement measures based on the collected opinions. For example, based on employee feedback, the system proposes specific improvement measures such as improving work processes and enhancing employee benefits. This allows organizations to implement effective improvements and increase employee satisfaction. This system not only improves employee satisfaction but also allows for real-time monitoring of the organization's state and the implementation of effective improvements. For instance, by collecting employee feedback, analyzing it with AI, and proposing improvements, employee dissatisfaction can be resolved early, and a more comfortable working environment can be provided. Furthermore, by referencing successful case studies from other organizations, effective improvements can be implemented quickly. This improves overall organizational productivity and enhances corporate value. In short, the employee satisfaction survey system can efficiently collect, analyze, and propose improvements based on employee feedback.

[0029] The employee satisfaction survey system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects employee opinions. The collection unit collects employee opinions by, for example, adding open-ended questions or an anonymous suggestion box. For example, the collection unit adds open-ended questions to the monthly survey, allowing employees to freely write their opinions. The collection unit also installs an anonymous suggestion box where employees can always submit their opinions, allowing them to submit complaints and requests anonymously. This allows the collection unit to collect detailed opinions from employees. The analysis unit analyzes the opinions collected by the collection unit, aggregates the results, and creates a report for reporting. For example, the analysis unit classifies the collected opinions and identifies common problems and areas for improvement. The analysis unit can analyze employee opinions using, for example, text mining technology. The analysis unit can also use statistical analysis technology to quantify employee opinions and generate data to understand the state of the organization. Furthermore, the analysis unit can use sentiment analysis technology to estimate emotions from employee opinions and understand the state of the organization. For example, the analysis unit analyzes employee opinions using text mining technology to identify common problems. The analysis unit can also use statistical analysis technology to quantify employee opinions and generate data to understand the state of the organization. Furthermore, the analysis unit can use sentiment analysis technology to estimate emotions from employee opinions and understand the state of the organization. The proposal unit proposes improvement measures based on the results obtained by the analysis unit. The proposal unit proposes improvement measures, for example, by referring to successful cases in other organizations. The proposal unit proposes specific improvement measures, such as improving business processes or enhancing employee benefits, based on employee opinions. The proposal unit can learn from successful cases in other organizations and propose optimal improvement measures based on the collected opinions. As a result, the employee satisfaction survey system according to this embodiment can efficiently collect, analyze, and propose improvement measures based on employee opinions.

[0030] The collection department collects employee opinions. For example, it might add open-ended questions or anonymous suggestion boxes to gather employee feedback. Specifically, it could add open-ended questions to monthly surveys, allowing employees to freely express their opinions. This enables employees to describe their thoughts and concerns in detail, leading to more specific feedback. The collection department could also set up an anonymous suggestion box where employees can submit complaints and requests anonymously. This provides an environment where employees can freely express their opinions, particularly for sensitive issues or personal grievances. This allows the collection department to gather detailed employee feedback. Furthermore, the collection department could utilize online platforms to allow employees to submit opinions anytime, anywhere. For example, a dedicated website or mobile app could be created to allow employees to easily post their opinions. This allows employees to submit opinions even outside of work hours, enabling the collection of more feedback. The collection department could also conduct regular focus groups and interviews to provide opportunities to directly hear employee opinions. This allows the data collection department to gather employee opinions from multiple perspectives and gain a deeper understanding.

[0031] The analysis department analyzes the opinions collected by the collection department, compiles the results, and creates reports. For example, the analysis department classifies the collected opinions and identifies common problems and areas for improvement. Specifically, it can analyze employee opinions using text mining technology. Text mining technology is a technique for extracting useful information from large amounts of text data, enabling efficient analysis of employee opinions. For example, natural language processing technology can be used to classify employee opinions by topic and identify common problems and areas for improvement. The analysis department can also use statistical analysis technology to quantify employee opinions and generate data to understand the state of the organization. For example, employee opinions can be scored, allowing for comparison of satisfaction levels across departments and teams. Furthermore, the analysis department can use sentiment analysis technology to estimate emotions from employee opinions and understand the state of the organization. Sentiment analysis technology automatically extracts emotions from text data, allowing for the identification of positive and negative emotions from employee opinions. For example, by analyzing employee opinions and calculating the ratio of positive to negative opinions, the overall emotional state of the organization can be understood. This allows the analysis unit to analyze the collected data from multiple perspectives and accurately understand the state of the organization. Furthermore, by comparing it with historical data and data from other organizations, the analysis unit can identify trends and patterns and formulate long-term improvement measures.

[0032] The proposal department proposes improvement measures based on the results obtained by the analysis department. For example, the proposal department proposes improvement measures by referring to successful cases in other organizations. Specifically, it proposes concrete improvement measures such as improving business processes and enhancing employee benefits based on employee opinions. For example, based on employee feedback, it can propose concrete measures to improve the efficiency of business processes and communication. Furthermore, the proposal department can learn from successful cases in other organizations and propose optimal improvement measures based on the collected opinions. For example, it can propose concrete measures to improve employee satisfaction, such as introducing flexible working hours or remote work, which are implemented in other companies. In addition, the proposal department can propose new employee benefit programs and training programs that reflect employee opinions. For example, it could introduce fitness programs or mental health support programs to support employee health management. In this way, the proposal department can propose concrete improvement measures based on employee opinions and improve overall organizational satisfaction. Furthermore, the proposal department can monitor the effectiveness of the proposed improvement measures and make revisions or additional suggestions as needed. In this way, the proposal department can continuously improve the organization and maintain and improve employee satisfaction.

[0033] The data collection unit can collect employee opinions by adding open-ended questions or anonymous suggestion boxes. For example, the data collection unit can add open-ended questions to monthly surveys, allowing employees to freely express their opinions. For example, the data collection unit can set up anonymous suggestion boxes where employees can submit their complaints and requests anonymously. The data collection unit can also collect employee opinions using online forms, for example. This allows the data collection unit to collect detailed employee opinions. Open-ended questions include, but are not limited to, free-response questions and short-answer questions. Anonymous suggestion boxes include, but are not limited to, online forms and physical suggestion boxes. This allows the data collection unit to collect detailed employee opinions. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can use generative AI to classify and summarize opinions in order to collect employee opinions.

[0034] The analysis unit can analyze collected opinions and generate data to understand the state of the organization. For example, the analysis unit can classify collected opinions and identify common problems and areas for improvement. For example, the analysis unit can analyze employee opinions using text mining techniques. The analysis unit can also quantify employee opinions using statistical analysis techniques and generate data to understand the state of the organization. Furthermore, the analysis unit can estimate emotions from employee opinions using sentiment analysis techniques to understand the state of the organization. For example, the analysis unit can analyze employee opinions using text mining techniques and identify common problems. The analysis unit can also quantify employee opinions using statistical analysis techniques and generate data to understand the state of the organization. Furthermore, the analysis unit can estimate emotions from employee opinions using sentiment analysis techniques to understand the state of the organization. This allows the analysis unit to understand the state of the organization in real time. The state of the organization includes, but is not limited to, employee satisfaction and work efficiency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected opinions into a generating AI, which can then classify and summarize those opinions.

[0035] The proposal department can propose improvement measures by referring to successful cases in other organizations. For example, the proposal department can learn from successful cases in other organizations and propose the optimal improvement measures based on collected opinions. For example, the proposal department can propose specific improvement measures such as improving business processes or enhancing employee benefits based on employee opinions. For example, the proposal department can use successful cases in other organizations as benchmarks and propose the optimal improvement measures based on collected opinions. In this way, the proposal department can propose effective improvement measures by referring to successful cases in other organizations. Successful cases in other organizations include, but are not limited to, benchmarks and case studies. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input successful cases in other organizations into a generating AI, and the generating AI can propose the optimal improvement measures.

[0036] The proposal department can propose specific improvement measures based on employee feedback, such as improving business processes and enhancing employee benefits. For example, the proposal department can propose improvements to business processes based on employee feedback. For example, the proposal department can propose enhancements to employee benefits based on employee feedback. For example, the proposal department can propose specific improvement measures based on employee feedback, such as improving business processes and enhancing employee benefits. This allows the proposal department to propose specific improvement measures based on employee feedback. Improvements to business processes include, but are not limited to, reviewing work procedures and introducing tools for efficiency. Enhancements to employee benefits include, but are not limited to, conducting health checkups and introducing refresh leave. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input employee feedback into a generating AI, which can then propose the most suitable improvement measures.

[0037] The collection unit can analyze employees' past opinion submission history and select the optimal collection method. For example, the collection unit can provide detailed questionnaires to employees who have submitted many opinions in the past. For example, the collection unit can collect opinions from employees who have submitted few opinions using simple question formats. For example, based on past opinion submission history, the collection unit can prioritize providing questions related to specific themes to employees who are interested in those themes. In this way, the collection unit can efficiently collect opinions by selecting the optimal collection method based on employees' past opinion submission history. Opinion submission history includes, but is not limited to, the number of submissions in the past and the trends in the content of submissions. Optimal collection methods include, but are not limited to, questionnaire formats and interview formats. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input employees' past opinion submission history into a generating AI, which can then select the optimal collection method.

[0038] The data collection unit can filter opinions based on employees' current projects and areas of interest. For example, the data collection unit can prioritize collecting opinions related to ongoing projects. For example, the data collection unit can provide questions related to employees' areas of interest and collect opinions. For example, the data collection unit can collect opinions at appropriate times depending on the progress of a project. This allows the data collection unit to collect highly relevant opinions by filtering them based on employees' current projects and areas of interest. Current projects include, but are not limited to, project names and project progress. Areas of interest include, but are not limited to, technical fields and business areas. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data on employees' current projects and areas of interest into a generating AI, which can then filter the opinions.

[0039] The collection unit can prioritize collecting highly relevant opinions by considering the geographical location information of employees when collecting opinions. For example, the collection unit can prioritize collecting opinions from employees working in a specific region. For example, the collection unit can collect opinions from employees who are geographically close to each other. For example, the collection unit can prioritize collecting opinions on issues in a specific region based on geographical location information. In this way, the collection unit can grasp region-specific issues by prioritizing the collection of highly relevant opinions based on the geographical location information of employees. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the geographical location information of employees into a generating AI, and the generating AI can determine the priority of opinions.

[0040] The collection unit can analyze employees' social media activity and collect relevant opinions when gathering feedback. For example, the collection unit can analyze the content of social media posts and collect relevant opinions. The collection unit can adjust the timing of feedback collection based on the frequency of social media activity. The collection unit can provide relevant questions and collect opinions based on social media interests. This allows the collection unit to understand employees' interests by collecting relevant opinions based on their social media activity. Social media activity includes, but is not limited to, analyzing post content and follower counts. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input employee social media activity data into a generating AI, which can then collect feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions during the analysis. For example, the analysis unit can perform a detailed analysis on opinions of high importance. For example, the analysis unit can perform a simplified analysis on opinions of low importance. The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions and provide appropriate feedback. Thus, the analysis unit can provide appropriate feedback by adjusting the level of detail of the analysis based on the importance of the opinions. The importance of an opinion is evaluated based on, for example, its impact or frequency, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input opinion importance data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the opinion during analysis. For example, the analysis unit can apply an analysis algorithm for improving business efficiency to opinions related to business processes. For example, the analysis unit can apply an analysis algorithm for improving employee satisfaction to opinions related to employee benefits. The analysis unit can select the optimal analysis algorithm according to the category of the opinion and provide appropriate feedback. In this way, the analysis unit can provide appropriate feedback by applying the optimal analysis algorithm according to the category of the opinion. The categories of opinions include, but are not limited to, technical opinions and opinions on business improvement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input opinion category data into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on when the opinions were submitted. For example, the analysis unit may prioritize the analysis of recently submitted opinions. For example, the analysis unit may postpone the analysis of older opinions. The analysis unit can determine the priority of analysis based on when the opinions were submitted and provide appropriate feedback. This allows the analysis unit to provide appropriate feedback by determining the priority of analysis based on when the opinions were submitted. The submission time of opinions includes, but is not limited to, the submission date and time or the frequency of submission. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the submission time data of opinions into a generating AI, and the generating AI can determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of opinions during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant opinions. For example, the analysis unit may postpone the analysis of less relevant opinions. The analysis unit can adjust the order of analysis based on the relevance of opinions and provide appropriate feedback. Thus, the analysis unit can provide appropriate feedback by adjusting the order of analysis based on the relevance of opinions. The relevance of opinions includes, but is not limited to, similarity of content or agreement on themes. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input opinion relevance data into a generating AI, and the generating AI may adjust the order of analysis.

[0045] The proposal department can adjust the level of detail of a proposal based on the importance of the improvement measures. For example, the proposal department can provide detailed proposals for high-importance improvement measures. For example, the proposal department can provide simplified proposals for low-importance improvement measures. The proposal department can adjust the level of detail of a proposal based on the importance of the improvement measures and provide appropriate feedback. This allows the proposal department to provide appropriate feedback by adjusting the level of detail of a proposal based on the importance of the improvement measures. The importance of improvement measures is evaluated based on, for example, impact and feasibility, but is not limited to such examples. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input improvement measure importance data into a generating AI, and the generating AI can adjust the level of detail of the proposal.

[0046] The proposal department can apply different proposal algorithms depending on the category of the improvement measure when making a proposal. For example, for improving business processes, the proposal department can apply a proposal algorithm for improving operational efficiency. For example, for enhancing employee benefits, the proposal department can apply a proposal algorithm for improving employee satisfaction. The proposal department can select the optimal proposal algorithm depending on the category of the improvement measure and provide appropriate feedback. In this way, the proposal department can provide appropriate feedback by applying the optimal proposal algorithm according to the category of the improvement measure. The categories of improvement measures include, but are not limited to, technical improvement measures and operational improvement measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input improvement measure category data into a generating AI, and the generating AI can apply the optimal proposal algorithm.

[0047] The proposal department can prioritize proposals based on when the improvement measures are submitted. For example, the proposal department may prioritize recently submitted improvement measures. For example, the proposal department may postpone the submission of older improvement measures. The proposal department can prioritize proposals based on when the improvement measures are submitted and provide appropriate feedback. This allows the proposal department to provide appropriate feedback by prioritizing proposals based on when the improvement measures are submitted. The submission timing of improvement measures includes, but is not limited to, the submission date and time or submission frequency. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input improvement measure submission timing data into a generating AI, which can then determine the priority of the proposals.

[0048] The proposal unit can adjust the order of proposals based on the relevance of the improvement measures when making proposals. For example, the proposal unit may prioritize proposing highly relevant improvement measures. For example, the proposal unit may postpone proposing less relevant improvement measures. The proposal unit can adjust the order of proposals based on the relevance of the improvement measures and provide appropriate feedback. This allows the proposal unit to provide appropriate feedback by adjusting the order of proposals based on the relevance of the improvement measures. The relevance of improvement measures includes, but is not limited to, similarity of content or matching of themes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit may input relevance data of improvement measures into a generating AI, and the generating AI may adjust the order of proposals.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can adjust the timing of employee opinion collection, taking into account the employees' work schedules. For example, it can collect opinions from night shift employees at night and from day shift employees during the day. Furthermore, it can adjust the timing of opinion collection for shift-based employees according to their shift changes. This allows the data collection unit to efficiently collect more opinions by adjusting the timing of collection according to employees' work schedules. Work schedules include, but are not limited to, day shifts, night shifts, and shift work. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input employee work schedule data into a generating AI, which can then adjust the timing of opinion collection.

[0051] The analysis unit can adjust its analysis method based on the employee's job description when analyzing employee opinions. For example, it can apply a technical analysis method to the opinions of technical employees, and an analysis method related to management tasks to the opinions of managerial employees. It can also apply an analysis method related to sales activities to the opinions of sales employees. This allows the analysis unit to provide more accurate analysis results by selecting the most appropriate analysis method according to the employee's job description. Job descriptions include, but are not limited to, technical, managerial, and sales positions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee job description data into a generating AI, which can then select the most appropriate analysis method.

[0052] The proposal department can propose improvement measures that align with the organization's culture and values, based on employee feedback. For example, it can propose improvements to communication methods that align with the organization's culture, based on employee feedback. It can also propose enhancements to employee benefits that align with the organization's values, based on employee feedback. Furthermore, it can propose improvements to business processes that align with the organization's culture, based on employee feedback. In this way, the proposal department can improve employee satisfaction by proposing improvements that align with the organization's culture and values. Organizational culture and values ​​include, but are not limited to, teamwork, innovation, and customer focus. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input organizational culture and values ​​data into a generating AI, which can then propose optimal improvement measures.

[0053] The data collection unit can adjust its data collection methods based on the employee's skill level. For example, it can provide detailed technical questions to highly skilled employees and basic questions to less skilled employees. It can also adjust the frequency of data collection according to the skill level. This allows the data collection unit to collect more relevant data by adjusting its data collection methods according to the employee's skill level. Skill levels include, but are not limited to, beginner, intermediate, and advanced levels. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee skill level data into a generating AI, which can then adjust the data collection methods.

[0054] The analysis unit can adjust the perspective of its analysis based on the employee's career stage when analyzing employee opinions. For example, it can apply an analysis perspective related to training and education to the opinions of new employees, and an analysis perspective related to career advancement and skill improvement to the opinions of mid-career employees. Furthermore, it can apply an analysis perspective related to leadership and management to the opinions of veteran employees. In this way, the analysis unit can provide more useful analysis results by selecting the optimal analysis perspective according to the employee's career stage. Career stages include, but are not limited to, new employees, mid-career employees, and veteran employees. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee career stage data into a generating AI, which can then select the optimal analysis perspective.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection department collects employee opinions. The collection department collects employee opinions by adding, for example, open-ended questions and anonymous suggestion boxes. For example, the collection department adds open-ended questions to monthly surveys, allowing employees to freely write their opinions. The collection department also sets up anonymous suggestion boxes where employees can always submit their opinions, allowing them to submit complaints and requests anonymously. This allows the collection department to collect detailed opinions from employees. Step 2: The analysis unit analyzes the opinions collected by the collection unit, compiles the results, and creates a report. The analysis unit, for example, classifies the collected opinions and identifies common problems and areas for improvement. The analysis unit can analyze employee opinions using, for example, text mining techniques. The analysis unit can also use statistical analysis techniques to quantify employee opinions and generate data to understand the state of the organization. Furthermore, the analysis unit can use sentiment analysis techniques to estimate emotions from employee opinions and understand the state of the organization. Step 3: The proposal department proposes improvement measures based on the results obtained by the analysis department. The proposal department proposes improvement measures by, for example, referring to successful cases in other organizations. The proposal department proposes specific improvement measures such as improving business processes or enhancing employee benefits based on employee feedback. The proposal department can, for example, learn from successful cases in other organizations and propose the optimal improvement measures based on the collected feedback.

[0057] (Example of form 2) The employee satisfaction survey system according to an embodiment of the present invention is a system that grasps employee opinions in real time and proposes effective improvement measures. This system collects employee opinions, an AI analyzes and aggregates the results, and creates a report for reporting. Furthermore, the AI ​​proposes specific improvement measures by referring to successful cases in other organizations. For example, open-ended questions and an anonymous suggestion box are added to collect employee opinions. Employees can freely write their opinions through open-ended questions in the monthly survey. In addition, an anonymous suggestion box is set up where employees can always submit their opinions, allowing them to submit complaints and requests anonymously. This makes it possible to collect detailed opinions from employees. Next, the AI ​​analyzes the collected opinions, aggregates the results, and creates a report for reporting. The AI ​​analyzes the collected opinions and generates data to understand the state of the organization. For example, it classifies employee opinions and identifies common problems and areas for improvement. This allows organizational managers and HR personnel to understand the state of the organization in real time. Furthermore, the AI ​​proposes specific improvement measures by referring to successful cases in other organizations. The AI ​​learns from successful cases in other organizations and proposes optimal improvement measures based on the collected opinions. For example, based on employee feedback, the system proposes specific improvement measures such as improving work processes and enhancing employee benefits. This allows organizations to implement effective improvements and increase employee satisfaction. This system not only improves employee satisfaction but also allows for real-time monitoring of the organization's state and the implementation of effective improvements. For instance, by collecting employee feedback, analyzing it with AI, and proposing improvements, employee dissatisfaction can be resolved early, and a more comfortable working environment can be provided. Furthermore, by referencing successful case studies from other organizations, effective improvements can be implemented quickly. This improves overall organizational productivity and enhances corporate value. In short, the employee satisfaction survey system can efficiently collect, analyze, and propose improvements based on employee feedback.

[0058] The employee satisfaction survey system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects employee opinions. The collection unit collects employee opinions by, for example, adding open-ended questions or an anonymous suggestion box. For example, the collection unit adds open-ended questions to the monthly survey, allowing employees to freely write their opinions. The collection unit also installs an anonymous suggestion box where employees can always submit their opinions, allowing them to submit complaints and requests anonymously. This allows the collection unit to collect detailed opinions from employees. The analysis unit analyzes the opinions collected by the collection unit, aggregates the results, and creates a report for reporting. For example, the analysis unit classifies the collected opinions and identifies common problems and areas for improvement. The analysis unit can analyze employee opinions using, for example, text mining technology. The analysis unit can also use statistical analysis technology to quantify employee opinions and generate data to understand the state of the organization. Furthermore, the analysis unit can use sentiment analysis technology to estimate emotions from employee opinions and understand the state of the organization. For example, the analysis unit analyzes employee opinions using text mining technology to identify common problems. The analysis unit can also use statistical analysis technology to quantify employee opinions and generate data to understand the state of the organization. Furthermore, the analysis unit can use sentiment analysis technology to estimate emotions from employee opinions and understand the state of the organization. The proposal unit proposes improvement measures based on the results obtained by the analysis unit. The proposal unit proposes improvement measures, for example, by referring to successful cases in other organizations. The proposal unit proposes specific improvement measures, such as improving business processes or enhancing employee benefits, based on employee opinions. The proposal unit can learn from successful cases in other organizations and propose optimal improvement measures based on the collected opinions. As a result, the employee satisfaction survey system according to this embodiment can efficiently collect, analyze, and propose improvement measures based on employee opinions.

[0059] The collection department collects employee opinions. For example, it might add open-ended questions or anonymous suggestion boxes to gather employee feedback. Specifically, it could add open-ended questions to monthly surveys, allowing employees to freely express their opinions. This enables employees to describe their thoughts and concerns in detail, leading to more specific feedback. The collection department could also set up an anonymous suggestion box where employees can submit complaints and requests anonymously. This provides an environment where employees can freely express their opinions, particularly for sensitive issues or personal grievances. This allows the collection department to gather detailed employee feedback. Furthermore, the collection department could utilize online platforms to allow employees to submit opinions anytime, anywhere. For example, a dedicated website or mobile app could be created to allow employees to easily post their opinions. This allows employees to submit opinions even outside of work hours, enabling the collection of more feedback. The collection department could also conduct regular focus groups and interviews to provide opportunities to directly hear employee opinions. This allows the data collection department to gather employee opinions from multiple perspectives and gain a deeper understanding.

[0060] The analysis department analyzes the opinions collected by the collection department, compiles the results, and creates reports. For example, the analysis department classifies the collected opinions and identifies common problems and areas for improvement. Specifically, it can analyze employee opinions using text mining technology. Text mining technology is a technique for extracting useful information from large amounts of text data, enabling efficient analysis of employee opinions. For example, natural language processing technology can be used to classify employee opinions by topic and identify common problems and areas for improvement. The analysis department can also use statistical analysis technology to quantify employee opinions and generate data to understand the state of the organization. For example, employee opinions can be scored, allowing for comparison of satisfaction levels across departments and teams. Furthermore, the analysis department can use sentiment analysis technology to estimate emotions from employee opinions and understand the state of the organization. Sentiment analysis technology automatically extracts emotions from text data, allowing for the identification of positive and negative emotions from employee opinions. For example, by analyzing employee opinions and calculating the ratio of positive to negative opinions, the overall emotional state of the organization can be understood. This allows the analysis unit to analyze the collected data from multiple perspectives and accurately understand the state of the organization. Furthermore, by comparing it with historical data and data from other organizations, the analysis unit can identify trends and patterns and formulate long-term improvement measures.

[0061] The proposal department proposes improvement measures based on the results obtained by the analysis department. For example, the proposal department proposes improvement measures by referring to successful cases in other organizations. Specifically, it proposes concrete improvement measures such as improving business processes and enhancing employee benefits based on employee opinions. For example, based on employee feedback, it can propose concrete measures to improve the efficiency of business processes and communication. Furthermore, the proposal department can learn from successful cases in other organizations and propose optimal improvement measures based on the collected opinions. For example, it can propose concrete measures to improve employee satisfaction, such as introducing flexible working hours or remote work, which are implemented in other companies. In addition, the proposal department can propose new employee benefit programs and training programs that reflect employee opinions. For example, it could introduce fitness programs or mental health support programs to support employee health management. In this way, the proposal department can propose concrete improvement measures based on employee opinions and improve overall organizational satisfaction. Furthermore, the proposal department can monitor the effectiveness of the proposed improvement measures and make revisions or additional suggestions as needed. In this way, the proposal department can continuously improve the organization and maintain and improve employee satisfaction.

[0062] The data collection unit can collect employee opinions by adding open-ended questions or anonymous suggestion boxes. For example, the data collection unit can add open-ended questions to monthly surveys, allowing employees to freely express their opinions. For example, the data collection unit can set up anonymous suggestion boxes where employees can submit their complaints and requests anonymously. The data collection unit can also collect employee opinions using online forms, for example. This allows the data collection unit to collect detailed employee opinions. Open-ended questions include, but are not limited to, free-response questions and short-answer questions. Anonymous suggestion boxes include, but are not limited to, online forms and physical suggestion boxes. This allows the data collection unit to collect detailed employee opinions. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can use generative AI to classify and summarize opinions in order to collect employee opinions.

[0063] The analysis unit can analyze collected opinions and generate data to understand the state of the organization. For example, the analysis unit can classify collected opinions and identify common problems and areas for improvement. For example, the analysis unit can analyze employee opinions using text mining techniques. The analysis unit can also quantify employee opinions using statistical analysis techniques and generate data to understand the state of the organization. Furthermore, the analysis unit can estimate emotions from employee opinions using sentiment analysis techniques to understand the state of the organization. For example, the analysis unit can analyze employee opinions using text mining techniques and identify common problems. The analysis unit can also quantify employee opinions using statistical analysis techniques and generate data to understand the state of the organization. Furthermore, the analysis unit can estimate emotions from employee opinions using sentiment analysis techniques to understand the state of the organization. This allows the analysis unit to understand the state of the organization in real time. The state of the organization includes, but is not limited to, employee satisfaction and work efficiency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected opinions into a generating AI, which can then classify and summarize those opinions.

[0064] The proposal department can propose improvement measures by referring to successful cases in other organizations. For example, the proposal department can learn from successful cases in other organizations and propose the optimal improvement measures based on collected opinions. For example, the proposal department can propose specific improvement measures such as improving business processes or enhancing employee benefits based on employee opinions. For example, the proposal department can use successful cases in other organizations as benchmarks and propose the optimal improvement measures based on collected opinions. In this way, the proposal department can propose effective improvement measures by referring to successful cases in other organizations. Successful cases in other organizations include, but are not limited to, benchmarks and case studies. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input successful cases in other organizations into a generating AI, and the generating AI can propose the optimal improvement measures.

[0065] The proposal department can propose specific improvement measures based on employee feedback, such as improving business processes and enhancing employee benefits. For example, the proposal department can propose improvements to business processes based on employee feedback. For example, the proposal department can propose enhancements to employee benefits based on employee feedback. For example, the proposal department can propose specific improvement measures based on employee feedback, such as improving business processes and enhancing employee benefits. This allows the proposal department to propose specific improvement measures based on employee feedback. Improvements to business processes include, but are not limited to, reviewing work procedures and introducing tools for efficiency. Enhancements to employee benefits include, but are not limited to, conducting health checkups and introducing refresh leave. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input employee feedback into a generating AI, which can then propose the most suitable improvement measures.

[0066] The data collection unit can estimate employees' emotions and adjust the timing of opinion collection based on those estimated emotions. For example, if an employee is feeling stressed, the data collection unit can collect opinions during times when the employee is relaxed. For example, the data collection unit can reduce the frequency of opinion collection during busy periods and increase it during less busy periods. For example, if an employee is experiencing positive emotions, the data collection unit can make it easier to collect opinions at those times. In this way, the data collection unit can collect more appropriate opinions by adjusting the timing of opinion collection according to employees' emotions. Employee emotions are estimated using, for example, emotion analysis tools or survey results, but are not limited to such examples. The timing of opinion collection includes, for example, regular collections or collections after events, but are not limited to such examples. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input employee emotion data into a generating AI, which can then adjust the timing of opinion collection.

[0067] The collection unit can analyze employees' past opinion submission history and select the optimal collection method. For example, the collection unit can provide detailed questionnaires to employees who have submitted many opinions in the past. For example, the collection unit can collect opinions from employees who have submitted few opinions using simple question formats. For example, based on past opinion submission history, the collection unit can prioritize providing questions related to specific themes to employees who are interested in those themes. In this way, the collection unit can efficiently collect opinions by selecting the optimal collection method based on employees' past opinion submission history. Opinion submission history includes, but is not limited to, the number of submissions in the past and the trends in the content of submissions. Optimal collection methods include, but are not limited to, questionnaire formats and interview formats. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input employees' past opinion submission history into a generating AI, which can then select the optimal collection method.

[0068] The data collection unit can filter opinions based on employees' current projects and areas of interest. For example, the data collection unit can prioritize collecting opinions related to ongoing projects. For example, the data collection unit can provide questions related to employees' areas of interest and collect opinions. For example, the data collection unit can collect opinions at appropriate times depending on the progress of a project. This allows the data collection unit to collect highly relevant opinions by filtering them based on employees' current projects and areas of interest. Current projects include, but are not limited to, project names and project progress. Areas of interest include, but are not limited to, technical fields and business areas. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data on employees' current projects and areas of interest into a generating AI, which can then filter the opinions.

[0069] The data collection unit can estimate employees' emotions and determine the priority of opinions to collect based on the estimated emotions. For example, if an employee is dissatisfied, the data collection unit will prioritize collecting that opinion. For example, if an employee is satisfied, the data collection unit may postpone collecting that opinion. For example, the data collection unit can prioritize collecting opinions of high importance based on employees' emotions. In this way, the data collection unit can prioritize collecting important opinions by prioritizing opinions based on employees' emotions. The priority of opinions is determined based on, for example, importance or urgency, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input employee emotion data into a generating AI, and the generating AI can determine the priority of opinions.

[0070] The collection unit can prioritize collecting highly relevant opinions by considering the geographical location information of employees when collecting opinions. For example, the collection unit can prioritize collecting opinions from employees working in a specific region. For example, the collection unit can collect opinions from employees who are geographically close to each other. For example, the collection unit can prioritize collecting opinions on issues in a specific region based on geographical location information. In this way, the collection unit can grasp region-specific issues by prioritizing the collection of highly relevant opinions based on the geographical location information of employees. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the geographical location information of employees into a generating AI, and the generating AI can determine the priority of opinions.

[0071] The collection unit can analyze employees' social media activity and collect relevant opinions when gathering feedback. For example, the collection unit can analyze the content of social media posts and collect relevant opinions. The collection unit can adjust the timing of feedback collection based on the frequency of social media activity. The collection unit can provide relevant questions and collect opinions based on social media interests. This allows the collection unit to understand employees' interests by collecting relevant opinions based on their social media activity. Social media activity includes, but is not limited to, analyzing post content and follower counts. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input employee social media activity data into a generating AI, which can then collect feedback.

[0072] The analysis unit can estimate employee emotions and adjust the presentation of the analysis based on the estimated employee emotions. For example, if an employee is dissatisfied, the analysis unit can provide analysis results that highlight problems. For example, if an employee is satisfied, the analysis unit can provide analysis results that highlight positive elements. The analysis unit can adjust the presentation of the analysis results based on employee emotions and provide appropriate feedback. Thus, the analysis unit can provide appropriate feedback by adjusting the presentation of the analysis based on employee emotions. Presentation methods of the analysis include, but are not limited to, graph displays and text displays. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input employee emotion data into a generating AI, and the generating AI can adjust the presentation of the analysis results.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions during the analysis. For example, the analysis unit can perform a detailed analysis on opinions of high importance. For example, the analysis unit can perform a simplified analysis on opinions of low importance. The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions and provide appropriate feedback. Thus, the analysis unit can provide appropriate feedback by adjusting the level of detail of the analysis based on the importance of the opinions. The importance of an opinion is evaluated based on, for example, its impact or frequency, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input opinion importance data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the opinion during analysis. For example, the analysis unit can apply an analysis algorithm for improving business efficiency to opinions related to business processes. For example, the analysis unit can apply an analysis algorithm for improving employee satisfaction to opinions related to employee benefits. The analysis unit can select the optimal analysis algorithm according to the category of the opinion and provide appropriate feedback. In this way, the analysis unit can provide appropriate feedback by applying the optimal analysis algorithm according to the category of the opinion. The categories of opinions include, but are not limited to, technical opinions and opinions on business improvement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input opinion category data into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0075] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee's emotions. For example, if the employee is dissatisfied, the analysis unit can provide a detailed analysis result. For example, if the employee is satisfied, the analysis unit can provide a concise analysis result. The analysis unit can adjust the length of the analysis result based on the employee's emotions and provide appropriate feedback. Thus, the analysis unit can provide appropriate feedback by adjusting the length of the analysis based on the employee's emotions. The length of the analysis includes, but is not limited to, detailed analysis and summary analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input employee emotion data into a generating AI, and the generating AI can adjust the length of the analysis result.

[0076] The analysis unit can determine the priority of analysis based on when the opinions were submitted. For example, the analysis unit may prioritize the analysis of recently submitted opinions. For example, the analysis unit may postpone the analysis of older opinions. The analysis unit can determine the priority of analysis based on when the opinions were submitted and provide appropriate feedback. This allows the analysis unit to provide appropriate feedback by determining the priority of analysis based on when the opinions were submitted. The submission time of opinions includes, but is not limited to, the submission date and time or the frequency of submission. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the submission time data of opinions into a generating AI, and the generating AI can determine the priority of analysis.

[0077] The analysis unit can adjust the order of analysis based on the relevance of opinions during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant opinions. For example, the analysis unit may postpone the analysis of less relevant opinions. The analysis unit can adjust the order of analysis based on the relevance of opinions and provide appropriate feedback. Thus, the analysis unit can provide appropriate feedback by adjusting the order of analysis based on the relevance of opinions. The relevance of opinions includes, but is not limited to, similarity of content or agreement on themes. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input opinion relevance data into a generating AI, and the generating AI may adjust the order of analysis.

[0078] The proposal department can estimate employees' emotions and adjust the way proposals are presented based on those estimated emotions. For example, if an employee is dissatisfied, the proposal department can make proposals that emphasize specific improvement measures. For example, if an employee is satisfied, the proposal department can make proposals that emphasize positive aspects. The proposal department can adjust the way proposals are presented based on employees' emotions and provide appropriate feedback. This allows the proposal department to provide appropriate feedback by adjusting the way proposals are presented based on employees' emotions. The presentation of proposals includes, but is not limited to, oral proposals and written proposals. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input employee emotion data into a generating AI, which can then adjust the way proposals are presented.

[0079] The proposal department can adjust the level of detail of a proposal based on the importance of the improvement measures. For example, the proposal department can provide detailed proposals for high-importance improvement measures. For example, the proposal department can provide simplified proposals for low-importance improvement measures. The proposal department can adjust the level of detail of a proposal based on the importance of the improvement measures and provide appropriate feedback. This allows the proposal department to provide appropriate feedback by adjusting the level of detail of a proposal based on the importance of the improvement measures. The importance of improvement measures is evaluated based on, for example, impact and feasibility, but is not limited to such examples. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input improvement measure importance data into a generating AI, and the generating AI can adjust the level of detail of the proposal.

[0080] The proposal department can apply different proposal algorithms depending on the category of the improvement measure when making a proposal. For example, for improving business processes, the proposal department can apply a proposal algorithm for improving operational efficiency. For example, for enhancing employee benefits, the proposal department can apply a proposal algorithm for improving employee satisfaction. The proposal department can select the optimal proposal algorithm depending on the category of the improvement measure and provide appropriate feedback. In this way, the proposal department can provide appropriate feedback by applying the optimal proposal algorithm according to the category of the improvement measure. The categories of improvement measures include, but are not limited to, technical improvement measures and operational improvement measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input improvement measure category data into a generating AI, and the generating AI can apply the optimal proposal algorithm.

[0081] The suggestion department can estimate an employee's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if an employee is dissatisfied, the suggestion department can provide a detailed suggestion. For example, if an employee is satisfied, the suggestion department can provide a concise suggestion. The suggestion department can adjust the length of the suggestion based on the employee's emotions and provide appropriate feedback. This allows the suggestion department to provide appropriate feedback by adjusting the length of the suggestion based on the employee's emotions. The length of the suggestion includes, but is not limited to, detailed suggestions and summary suggestions. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input employee emotion data into a generating AI, which can then adjust the length of the suggestion.

[0082] The proposal department can prioritize proposals based on when the improvement measures are submitted. For example, the proposal department may prioritize recently submitted improvement measures. For example, the proposal department may postpone the submission of older improvement measures. The proposal department can prioritize proposals based on when the improvement measures are submitted and provide appropriate feedback. This allows the proposal department to provide appropriate feedback by prioritizing proposals based on when the improvement measures are submitted. The submission timing of improvement measures includes, but is not limited to, the submission date and time or submission frequency. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input improvement measure submission timing data into a generating AI, which can then determine the priority of the proposals.

[0083] The proposal unit can adjust the order of proposals based on the relevance of the improvement measures when making proposals. For example, the proposal unit may prioritize proposing highly relevant improvement measures. For example, the proposal unit may postpone proposing less relevant improvement measures. The proposal unit can adjust the order of proposals based on the relevance of the improvement measures and provide appropriate feedback. This allows the proposal unit to provide appropriate feedback by adjusting the order of proposals based on the relevance of the improvement measures. The relevance of improvement measures includes, but is not limited to, similarity of content or matching of themes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit may input relevance data of improvement measures into a generating AI, and the generating AI may adjust the order of proposals.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The data collection unit can adjust the timing of employee opinion collection, taking into account the employees' work schedules. For example, it can collect opinions from night shift employees at night and from day shift employees during the day. Furthermore, it can adjust the timing of opinion collection for shift-based employees according to their shift changes. This allows the data collection unit to efficiently collect more opinions by adjusting the timing of collection according to employees' work schedules. Work schedules include, but are not limited to, day shifts, night shifts, and shift work. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input employee work schedule data into a generating AI, which can then adjust the timing of opinion collection.

[0086] The analysis unit can adjust its analysis method based on the employee's job description when analyzing employee opinions. For example, it can apply a technical analysis method to the opinions of technical employees, and an analysis method related to management tasks to the opinions of managerial employees. It can also apply an analysis method related to sales activities to the opinions of sales employees. This allows the analysis unit to provide more accurate analysis results by selecting the most appropriate analysis method according to the employee's job description. Job descriptions include, but are not limited to, technical, managerial, and sales positions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee job description data into a generating AI, which can then select the most appropriate analysis method.

[0087] The proposal department can propose improvement measures that align with the organization's culture and values, based on employee feedback. For example, it can propose improvements to communication methods that align with the organization's culture, based on employee feedback. It can also propose enhancements to employee benefits that align with the organization's values, based on employee feedback. Furthermore, it can propose improvements to business processes that align with the organization's culture, based on employee feedback. In this way, the proposal department can improve employee satisfaction by proposing improvements that align with the organization's culture and values. Organizational culture and values ​​include, but are not limited to, teamwork, innovation, and customer focus. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input organizational culture and values ​​data into a generating AI, which can then propose optimal improvement measures.

[0088] The data collection unit can adjust its data collection methods based on the employee's skill level. For example, it can provide detailed technical questions to highly skilled employees and basic questions to less skilled employees. It can also adjust the frequency of data collection according to the skill level. This allows the data collection unit to collect more relevant data by adjusting its data collection methods according to the employee's skill level. Skill levels include, but are not limited to, beginner, intermediate, and advanced levels. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee skill level data into a generating AI, which can then adjust the data collection methods.

[0089] The analysis unit can adjust the perspective of its analysis based on the employee's career stage when analyzing employee opinions. For example, it can apply an analysis perspective related to training and education to the opinions of new employees, and an analysis perspective related to career advancement and skill improvement to the opinions of mid-career employees. Furthermore, it can apply an analysis perspective related to leadership and management to the opinions of veteran employees. In this way, the analysis unit can provide more useful analysis results by selecting the optimal analysis perspective according to the employee's career stage. Career stages include, but are not limited to, new employees, mid-career employees, and veteran employees. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee career stage data into a generating AI, which can then select the optimal analysis perspective.

[0090] The data collection unit can estimate employees' emotions and adjust the method of collecting opinions based on the estimated emotions. For example, if an employee is stressed, opinions may be collected using simple questions. If an employee is relaxed, for example, opinions may be collected using detailed questions. Also, if an employee is experiencing positive emotions, the data collection unit can make it easier to collect opinions at that time. In this way, the data collection unit can collect more appropriate opinions by adjusting the method of collecting opinions according to the emotions of the employees. Employee emotions may be estimated using, for example, emotion analysis tools or survey results, but are not limited to such examples. Methods of collecting opinions may include, for example, simple question formats or detailed question formats, but are not limited to such examples. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit may input employee emotion data into a generating AI, which can then adjust the method of collecting opinions.

[0091] The analysis unit can estimate employees' emotions and adjust the analysis perspective based on the estimated emotions. For example, if an employee is dissatisfied, it can provide an analysis perspective that emphasizes problems. If an employee is satisfied, it can provide an analysis perspective that emphasizes positive elements. The analysis unit can also adjust how the analysis results are presented based on the employee's emotions and provide appropriate feedback. This allows the analysis unit to provide appropriate feedback by adjusting the analysis perspective based on the employee's emotions. The analysis perspective includes, but is not limited to, emphasizing problems or emphasizing positive elements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee emotion data into a generating AI, which can then adjust the analysis perspective.

[0092] The suggestion department can estimate employees' emotions and adjust the timing of suggestions based on those emotions. For example, if an employee is feeling stressed, suggestions can be made during times when they are relaxed. The suggestion department can also reduce the frequency of suggestions during busy periods and increase them during less busy periods. Furthermore, the suggestion department can make suggestions more likely when employees are experiencing positive emotions. This allows the suggestion department to make more effective suggestions by adjusting the timing according to employees' emotions. Employee emotions are estimated using, for example, emotion analysis tools or survey results. Suggestion timing includes, for example, regular suggestions or suggestions after events. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not. For example, the suggestion department can input employee emotion data into a generating AI, which can then adjust the timing of suggestions.

[0093] The data collection unit can estimate employees' emotions and determine the priority of opinions to collect based on the estimated emotions. For example, if an employee is dissatisfied, the data collection unit will prioritize collecting that opinion. For example, if an employee is satisfied, the data collection unit may postpone collecting that opinion. For example, the data collection unit can prioritize collecting opinions of high importance based on employees' emotions. In this way, the data collection unit can prioritize collecting important opinions by prioritizing opinions based on employees' emotions. The priority of opinions is determined based on, for example, importance or urgency, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input employee emotion data into a generating AI, and the generating AI can determine the priority of opinions.

[0094] The proposal department can estimate employees' emotions and adjust the way proposals are presented based on those estimated emotions. For example, if an employee is dissatisfied, the proposal department can make proposals that emphasize specific improvement measures. For example, if an employee is satisfied, the proposal department can make proposals that emphasize positive aspects. The proposal department can adjust the way proposals are presented based on employees' emotions and provide appropriate feedback. This allows the proposal department to provide appropriate feedback by adjusting the way proposals are presented based on employees' emotions. The presentation of proposals includes, but is not limited to, oral proposals and written proposals. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input employee emotion data into a generating AI, which can then adjust the way proposals are presented.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection department collects employee opinions. The collection department collects employee opinions by adding, for example, open-ended questions and anonymous suggestion boxes. For example, the collection department adds open-ended questions to monthly surveys, allowing employees to freely write their opinions. The collection department also sets up anonymous suggestion boxes where employees can always submit their opinions, allowing them to submit complaints and requests anonymously. This allows the collection department to collect detailed opinions from employees. Step 2: The analysis unit analyzes the opinions collected by the collection unit, compiles the results, and creates a report. The analysis unit, for example, classifies the collected opinions and identifies common problems and areas for improvement. The analysis unit can analyze employee opinions using, for example, text mining techniques. The analysis unit can also use statistical analysis techniques to quantify employee opinions and generate data to understand the state of the organization. Furthermore, the analysis unit can use sentiment analysis techniques to estimate emotions from employee opinions and understand the state of the organization. Step 3: The proposal department proposes improvement measures based on the results obtained by the analysis department. The proposal department proposes improvement measures by, for example, referring to successful cases in other organizations. The proposal department proposes specific improvement measures such as improving business processes or enhancing employee benefits based on employee feedback. The proposal department can, for example, learn from successful cases in other organizations and propose the optimal improvement measures based on the collected feedback.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee opinions using the reception device 38 and camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected opinions, aggregates the results, and creates a report for reporting. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, which proposes specific improvement measures based on successful examples from other organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee opinions using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected opinions, aggregates the results, and creates a report for reporting. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes specific improvement measures based on successful examples from other organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee opinions using the microphone 238 and camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected opinions, aggregates the results, and creates a report for reporting. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, which proposes specific improvement measures based on successful examples from other organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee opinions using the microphone 238 and camera 42 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected opinions, aggregates the results, and creates a report for reporting. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes specific improvement measures based on successful cases in other organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A department that collects employee opinions, An analysis unit analyzes the opinions collected by the collection unit, compiles the results, and prepares a report for reporting. The system includes a proposal unit that proposes improvement measures based on the results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We will collect employee opinions by adding open-ended questions and an anonymous suggestion box. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected opinions are analyzed to generate data that helps understand the state of the organization. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose improvement measures based on successful examples from other organizations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on employee feedback, we propose specific improvement measures such as improving business processes and enhancing employee benefits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of opinion gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze employees' past feedback submission history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting opinions, filter them based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Estimate employee sentiment and prioritize the opinions to collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting opinions, we prioritize collecting highly relevant opinions by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering feedback, analyze employees' social media activity and collect relevant opinions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of each opinion. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the opinion. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates employee sentiment and adjusts the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the opinions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the order of analysis will be adjusted based on the relevance of the opinions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the employees' emotions and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the improvement measure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the employee's feelings and adjust the length of the suggestion based on those feelings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of the submission of improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of the suggestions based on the relevance of the improvement measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A department that collects employee opinions, An analysis unit analyzes the opinions collected by the collection unit, compiles the results, and prepares a report for reporting. The system includes a proposal unit that proposes improvement measures based on the results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We will collect employee opinions by adding open-ended questions and an anonymous suggestion box. The system according to feature 1.

3. The aforementioned analysis unit, The collected opinions are analyzed to generate data that helps understand the state of the organization. The system according to feature 1.

4. The aforementioned proposal section is, We propose improvement measures based on successful examples from other organizations. The system according to feature 1.

5. The aforementioned proposal section is, Based on employee feedback, we propose specific improvement measures such as improving business processes and enhancing employee benefits. The system according to feature 1.

6. The aforementioned collection unit is We estimate employees' emotions and adjust the timing of opinion gathering based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze employees' past feedback submission history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting opinions, filter them based on employees' current projects and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is Estimate employee sentiment and prioritize the opinions to collect based on that estimated sentiment. The system according to feature 1.

10. The aforementioned collection unit is When collecting opinions, we prioritize collecting highly relevant opinions by considering the geographical location of employees. The system according to feature 1.

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